PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images
PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images
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DOI：
10.1681/asn.2021050630
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发表时间：
2021-11-01
影响因子：
13.6
通讯作者：
Sarder, Pinaki
中科院分区：
文献类型：
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作者：
Govind, Darshana;Becker, Jan U.;Sarder, Pinaki
背景 在几种肾脏疾病中，足细胞耗竭先于进行性肾小球损伤。然而，目前从明场显微镜图像中视觉检测和定量足细胞核的标准既费力又不精确。方法我们开发了 PodoSighter，一种基于云的在线工具，利用深度学习自动识别和定量来自千兆像素明场全切片图像 (WSI) 的足细胞核。训练该工具的基本事实使用了来自小鼠、大鼠和人类肾脏的 122 个组织学切片的多机构队列的免疫组织化学或免疫荧光标记图像。为了证明我们的工具在研究临床相关样本中足细胞丢失方面的普遍性，我们在肾小球疾病的啮齿动物模型中进行了测试，包括糖尿病肾病、新月体肾小球肾炎和剂量依赖性直接足细胞毒性和耗竭，以及来自类固醇抵抗性肾病综合征和人体尸检组织的人体活检。结果最佳模型产生了高灵敏度/特异性，分别为0.80/0.80、0.81/0.86、在高碘酸希夫染色 WSI 的小鼠、大鼠和人类图像中，分别为 0.80/0.91 和 0.80/0.91。此外，使用 PodoSighter 提取的足细胞核形态计量学对于识别患病肾小球提供了信息。我们已将 PodoSighter 作为最终用户基于云的 Web 应用程序中的交钥匙插件免费提供给公众。结论我们的研究展示了一种自动计算方法来检测和量化标准组织学染色 WSI 中的足细胞核，促进足细胞研究，并实现未来可能的临床应用。
Background Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise.Methods We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate the generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic GN, and dose-dependent direct podocyte toxicity and depletion, and in human biopsies from steroid-resistant nephrotic syndrome and from human autopsy tissues.Results The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic acid-Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end users.Conclusions Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically stained WSIs, facilitating podocyte research, and enabling possible future clinical applications.